SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities
Introduction Social isolation has been found to be a significant risk factor for health outcomes, on par with traditional risk factors. This isolation is characterised by reduced social interactions, which can be detected acoustically. To accomplish this, we created a machine learning algorithm call...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2023-08-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/8/e076297.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846168083097452544 |
|---|---|
| author | Matthias R Mehl Ross Zafonte Kelly White Samuel Tate Shrikanth Narayanan Min Shin Amar Dhand |
| author_facet | Matthias R Mehl Ross Zafonte Kelly White Samuel Tate Shrikanth Narayanan Min Shin Amar Dhand |
| author_sort | Matthias R Mehl |
| collection | DOAJ |
| description | Introduction Social isolation has been found to be a significant risk factor for health outcomes, on par with traditional risk factors. This isolation is characterised by reduced social interactions, which can be detected acoustically. To accomplish this, we created a machine learning algorithm called SocialBit. SocialBit runs on a smartwatch and detects minutes of social interaction based on vocal features from ambient audio samples without natural language processing.Methods and analysis In this study, we aim to validate the accuracy of SocialBit in stroke survivors with varying speech, cognitive and physical deficits. Training and testing on persons with diverse neurological abilities allows SocialBit to be a universally accessible social sensor. We are recruiting 200 patients and following them for up to 8 days during hospitalisation and rehabilitation, while they wear a SocialBit-equipped smartwatch and engage in naturalistic daily interactions. Human observers tally the interactions via a video livestream (ground truth) to analyse the performance of SocialBit against it. We also examine the association of social interaction time with stroke characteristics and outcomes. If successful, SocialBit would be the first social sensor available on commercial devices for persons with diverse abilities.Ethics and dissemination This study has received ethical approval from the Institutional Review Board of Mass General Brigham (Protocol #2020P003739). The results of this study will be published in a peer-reviewed journal. |
| format | Article |
| id | doaj-art-084b402b9671420da976faebbea0a28e |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-084b402b9671420da976faebbea0a28e2024-11-14T06:45:08ZengBMJ Publishing GroupBMJ Open2044-60552023-08-0113810.1136/bmjopen-2023-076297SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilitiesMatthias R Mehl0Ross Zafonte1Kelly White2Samuel Tate3Shrikanth Narayanan4Min Shin5Amar Dhand6Department of Psychology, University of Arizona, Tucson, Arizona, USA1 Spaulding Rehabilitation Hospital, Charlestown, Massachusetts, USADepartment of Neurology, Brigham and Women`s Hospital, Boston, Massachusetts, USADepartment of Computer Science, The University of North Carolina at Charlotte, Charlotte, North Carolina, USADepartment of Engineering, University of Southern California, Los Angeles, California, USADepartment of Computer Science, The University of North Carolina at Charlotte, Charlotte, North Carolina, USADivision of Neurology, Brigham and Women`s Hospital, Boston, Massachusetts, USAIntroduction Social isolation has been found to be a significant risk factor for health outcomes, on par with traditional risk factors. This isolation is characterised by reduced social interactions, which can be detected acoustically. To accomplish this, we created a machine learning algorithm called SocialBit. SocialBit runs on a smartwatch and detects minutes of social interaction based on vocal features from ambient audio samples without natural language processing.Methods and analysis In this study, we aim to validate the accuracy of SocialBit in stroke survivors with varying speech, cognitive and physical deficits. Training and testing on persons with diverse neurological abilities allows SocialBit to be a universally accessible social sensor. We are recruiting 200 patients and following them for up to 8 days during hospitalisation and rehabilitation, while they wear a SocialBit-equipped smartwatch and engage in naturalistic daily interactions. Human observers tally the interactions via a video livestream (ground truth) to analyse the performance of SocialBit against it. We also examine the association of social interaction time with stroke characteristics and outcomes. If successful, SocialBit would be the first social sensor available on commercial devices for persons with diverse abilities.Ethics and dissemination This study has received ethical approval from the Institutional Review Board of Mass General Brigham (Protocol #2020P003739). The results of this study will be published in a peer-reviewed journal.https://bmjopen.bmj.com/content/13/8/e076297.full |
| spellingShingle | Matthias R Mehl Ross Zafonte Kelly White Samuel Tate Shrikanth Narayanan Min Shin Amar Dhand SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities BMJ Open |
| title | SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities |
| title_full | SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities |
| title_fullStr | SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities |
| title_full_unstemmed | SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities |
| title_short | SocialBit: protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities |
| title_sort | socialbit protocol for a prospective observational study to validate a wearable social sensor for stroke survivors with diverse neurological abilities |
| url | https://bmjopen.bmj.com/content/13/8/e076297.full |
| work_keys_str_mv | AT matthiasrmehl socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities AT rosszafonte socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities AT kellywhite socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities AT samueltate socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities AT shrikanthnarayanan socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities AT minshin socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities AT amardhand socialbitprotocolforaprospectiveobservationalstudytovalidateawearablesocialsensorforstrokesurvivorswithdiverseneurologicalabilities |